The healthcare sector can gradually embrace artificial intelligence (AI) to better leverage data, automate processes and systems, and ultimately enhance patient care.
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a Recent reports According to Statista, the global healthcare AI market is expected to reach approximately $188 billion by 2030.
“The healthcare industry is using machine learning and other emerging technologies to predict disease, develop medicines, perform AI-assisted surgery and medical imaging, and more,” he shares. Fani Teja Naramotu, Machine Learning Operations (MLOps) Senior Engineer.
However, successful operationalization of AI at scale in healthcare requires mission-critical capabilities.
Put your machine learning model into production
According to Teja, scaling AI requires a specific approach that includes three specialties: machine learning, data engineering, and development operations. All three areas need to work efficiently for a successful machine learning model implementation.
Data scientists employ machine learning when connecting to central data sources provided by data engineers to train, validate, tune hyperparameters, and retrain models. Scalable machine learning is primarily driven by deep learning algorithms. Deep learning algorithms take complex data, simplify it, and identify anomalies.
This specialization connects disparate data sources across an organization to collect, clean, transform, and store them in a centralized location. Data scientists use this information to train machine learning algorithms. Data collection and cleansing need to be efficient. When lag occurs, data scientists need more information to train their models.
3. Development work (DevOps)
In DevOps, engineers perform all the tasks required to operate trained machine learning models. This area includes provisioning infrastructure, creating her CI/CD pipeline for deployment, and configuring observability tools for operational models. Strong DevOps is essential for efficiently deploying hundreds of machine learning models and virtually sustaining the business value generated by data science models.
Streamlining Processes with MLOps Engineers
To address the issues of efficiency and effectiveness of AI systems, organizations rely on MLOps engineers like Teja, who are experts in three areas.
“Healthcare companies and other large organizations with complex technical requirements are looking to MLOps engineers who can do the work of data engineers, data scientists, and DevOps engineers,” explains Teja.
Adoption of AI in healthcare remains challenging, but hiring MLOps engineers can positively impact and advance digitization goals.
Teja is an expert in developing MLOps platforms for large organizations in healthcare and other major industries. He has a broad skill set that spans machine learning, data engineering, development and operations, allowing him to build his platform for deployment. These include a variety of applications, scalable database solutions, observability tools, CI/CD pipelines, infrastructure automation, and other development platforms for software engineers and data scientists.
The future of MLOps in healthcare
The potential for MLOps in healthcare is exciting and far-reaching. This niche sector helps healthcare organizations automate manual processes, reduce costs, and improve quality of care. It can also be used to identify areas for improvement within healthcare organizations so that data and resources can be better managed.
In the future, medical professionals can use MLOps to automate decision-making, reduce administrative costs, enable predictive analytics, and deliver personalized healthcare services.Teja‘MLOps expertise will play an even more important role in the future in managing, securing and optimizing the data and machine learning models that power AI-driven initiatives.
